The fusion of multispectral and synthetic aperture radar (SAR) images is of vital importance in many remote sensing applications. Spectral distortion and trade-off between the spatial and spectral quality of the fused image are significant issues in SAR-multispectral image fusion. Our study attempts to improve the performance of SAR-multispectral image fusion concerning these two issues. The primary objective of our study is to optimize the performance of hybrid fusion approach based on principal component analysis and discrete wavelet transform (PCA-DWT) using Taguchi orthogonal array. The fused data are evaluated using visual analysis and standard quality metrics. The results are compared with recent hybrid fusion approaches applied to the SAR-multispectral image fusion. The utility of the fused data is evaluated based on the remote sensing application, namely, land use land cover classification. The classification results are compared to a standard thematic map available on the Bhuvan geoportal to check classification accuracy. A comparative analysis with recent hybrid approaches conclusively demonstrates that the proposed optimization in the PCA-DWT based fusion is superior to conventional hybrid methods.

中文翻译：

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Taguchi方法在基于PCA-DWT的SAR多光谱图像融合改善土地利用土地覆盖分类中的应用

在许多遥感应用中，多光谱和合成孔径雷达（SAR）图像的融合至关重要。光谱失真和融合图像的空间质量与光谱质量之间的权衡是SAR多光谱图像融合中的重要问题。我们的研究试图针对这两个问题提高SAR多光谱图像融合的性能。我们研究的主要目标是基于主成分分析和使用Taguchi正交阵列的离散小波变换（PCA-DWT）优化混合​​融合方法的性能。使用视觉分析和标准质量指标评估融合的数据。将结果与最近应用于SAR多光谱图像融合的混合融合方法进行了比较。基于遥感应用（即土地利用，土地覆盖分类）评估融合数据的效用。将分类结果与Bhuvan地理门户网站上提供的标准主题地图进行比较，以检查分类准确性。与最近的混合方法的比较分析最终表明，在基于PCA-DWT的融合中所提出的优化优于传统的混合方法。